一种新的基于蚁群行为的多细胞跟踪算法

Qinglan Chen, Benlian Xu, Mingli Lu
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引用次数: 0

摘要

本文提出了一种利用智能蚂蚁系统进行多细胞跟踪的新框架,该框架首先提出了一个先验的蚁群分布块,通过背景核密度概率估计将出生蚂蚁直接放置在当前图像的相关像素上;然后,根据启发式直方图相似度和像素信息素水平,利用适当的蒸发和传播模型,开发多群体重建块,进一步将蚂蚁吸引到潜在区域;最后,实现了一个细胞状态提取块,自适应地确定细胞的数量和各自的状态。在真实细胞图像序列上的实验结果表明,该算法比其他方法具有更高的精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel multi-cell tracking algorithm based on ant colony behavior
This paper aims to develop a novel framework of multi-cell tracking using intelligent ant system, in which a priori colony distribution block is first proposed to directly place birth ants on relevant pixels of current image through kernel density probability estimate of background; afterwards, a multi-colony reconstruction block is developed to further attract ants towards potential regions according to heuristic histogram similarity and pixel pheromone level with an appropriate evaporation and propagation model; finally, a cell state extraction block is implemented to adaptively determine the number of cells and their individual states. Experiment results on real cell image sequences demonstrate that our algorithm could give a more accurate and robust performance than other methods.
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